Architecture Technology Corporation

Evan Eaves

William Colligan

Eric R Chartier

Andrew Murphy

Drew Sullivan

Paul C Davis

Douglas Sweet

Andrew Sullivan

A computer-implemented method for securing unmanned aerial system (UAS) operations includes receiving a UAS flight plan for a UAS and a UAS operation, the UAS flight plan including a flight profile and flight path for the UAS; determining a mission type for the UAS operation requires use of dummy aircraft information; and assigning a dummy UAS identification for the UAS. Generating dummy airframe information, including dummy airframe characteristics and performance data, for the UAS, includes generating dummy airframe information that corresponds to airframe information for an actual civil aircraft that could follow the received UAS flight plan. The method further includes causing the UAS to broadcast the dummy UAS identification and the dummy airframe information with an automatic dependent surveillance-broadcast signal during at least a portion of the UAS operation.

System and method for unmanned aerial system (UAS) modernization for avoidance and detection

An automatic, autonomous, and aircraft-centric interference advisory method is executed entirely on a fist aircraft operating on a movement area of a runway, the movement are including ramps, taxiways, and runways. The method includes a processor onboard the first aircraft computing a first movement projection for the first aircraft using first aircraft data received at the first aircraft; the processor computing additional second movement projections for multiple second aircraft operating on the movement area of the airport using second data regarding each of the multiple second aircraft; the processor detecting a threat to the first aircraft on approach to a defined intersection of the movement area from any of the multiple second aircraft based on a corresponding second movement projection within a configurable time limit of entry into the defined intersection by the first aircraft; and providing on the first aircraft, a threat advisory for a detected threat.

Advisor system and method

A product obsolescence forecast system includes machine instructions stored in a non-transitory computer readable storage medium, the machine instructions. A processor executes the instructions to receive an identity of a first product and identities of one or more second products similar to the first product, each of the second products having gone obsolete; receive a determinant of obsolescence of one or more of the obsolete second products; generate one or more observations related to the first product by inputting each received determinant to a trained network; and generate a statistical analysis of the one or more observations to provide an expected value of an actual obsolescence date for the first product.

Product obsolescence forecast system and method

An automatic, autonomous, and aircraft-centric interference advisory method is executed entirely on a first aircraft operating on a movement area of a runway, the movement are including ramps, taxiways, and runways. The method includes a processor onboard the first aircraft computing a first movement projection for the first aircraft using first aircraft data received at the first aircraft; the processor computing additional second movement projections for multiple second aircraft operating on the movement area of the airport using second data regarding each of the multiple second aircraft; the processor detecting a threat to the first aircraft on approach to a defined intersection of the movement area from any of the multiple second aircraft based on a corresponding second movement projection within a configurable time limit of entry into the defined intersection by the first aircraft; and providing on the first aircraft, a threat advisory for a detected threat.

A computer-implemented method for training a neural network system to classify data items as true mentions of relevant data objects includes, based on a domain of interest, a processor controlling an input component and a data store to receive and store a document related to the domain of interest, control a natural language processor to extract data items related to the domain of interest from the document, create a first training data set of selected ones of the data items, using the first training data set, train the neural network to identify true mentions of data objects relevant to the domain of interest, apply tweaks to one or more of the data items to create a list of tweaked data items, creating a second training data set including the tweaked data items, and using the first and second training data sets, further train the neural network to identify true mentions of relevant data objects.

Community-based reporting and analysis system and method

A method for securing flight operations of an unmanned aerial system (UAS) includes a processor receiving a flight operation for a UAS, the flight operation defining a UAS flight profile; and the processor, based on a designation of the flight operation as sensitive, controlling an automatic dependent surveillance-broadcast (ADS-B) transponder on the UAS to broadcast a dummy aircraft identification different from an ICAO-assigned transponder code, and dummy airframe information during at least a portion of the flight operation.

A product obsolescence forecast system comprises machine instructions stored in a non-transitory computer readable storage medium. A processor executes the machine instructions to access data items related to a first product and one or more second products designated as similar to the first product, the first product and each of the one or more second products belonging to a same class of products; extract data elements from the data items; reformat the data elements as analyzable data elements and store the analyzable data elements in an analyzable data structure; apply a probabilistic model to selected ones of the analyzable data elements to provide a forecast obsolescence date for the first product; and using the forecast obsolescence date, determine one or more impacts based on the forecast obsolescence date.

An advisor system includes a computer-readable storage medium having encoded thereon a program of instructions. Execution of the instructions causes a processor to determine a current state of a first aircraft operating on a movement area of an airport including determining a path vector for the first aircraft. The path vector includes a speed and direction of travel of the first aircraft and identification of a runway intersection the first aircraft is projected to enter. The processor processes a surveillance signal transmitted from a second aircraft operating on the movement area, including determining a quality of the surveillance signal. The processor further determines a movement vector of the second aircraft, and compares the path vector and the movement vector to identify possible interference. Finally, the processor provides an advisory at the first aircraft based on the compared path vector and the movement vector.

A computer-implemented method for analyzing documents includes a processor receiving one or more documents, from a community-based document delivery system, related to a domain of interest; the processor identifying and extracting one or more data items from the one or more documents; determining if an identified and extracted data item comprises a true mention of a named entity; analyzing a context of the true mention of the named entity in the document; and determining, based on the analyzed context, if the document is a true document.

A mobile emergency perimeter system includes one or more fixed radio frequency (RF) sensors, and one or more moving RF sensors; a wireless mesh network coupling together each of the sensors; and a central processor system coupled to the wireless mesh network. The central processor system executes machine instructions stored on a computer-readable storage medium to control each of the RF sensors to receive time of arrival data for a RF signal transmitted by an emitter and received at each of the RF sensors, receive location information for each of the RF sensors and determine a three-dimensional (3-D) estimate of the geographical location of the emitter.

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Alexandria, VA, United States of America

Evan Eaves